The present invention relates to data analytics, and more specifically, to privacy enhanced spatial and temporal analytics. Some analytics associate entities (such as ships) with their features (such as gross tonnage) and feature elements (such as unit of measure and gross weight).
Spatial and temporal analytics further allow entities to be associated with space and time data. Some spatial and temporal analytics use an entity feature known as a SpaceTimeBox (STB). An STB reflects a spatial region and a time interval, at a specific granularity. Any event, that is, any point in spacetime specified by its time and place, can be assigned to at least one STB. When an entity, such as a ship, is associated with an event, other entities can be compared with the entity and be associated with that entity's spatial location at a certain time at a granularity defined by the STB's granularity. In many cases, the STB granularity is configurable, as are parameters that allow for filtering of STBs in various conditions. The STB functionality provides spatial and temporal reasoning capabilities for advanced entity resolution, relationship awareness, and insight/relevance detection.
Motion processing typically relies on quantization of space and time, thus making STBs useful for this purpose as well. The motion of entities with respect to STBs can be used to detect specific entity behavior, in real time, which can be published to downstream analytic applications. The activities of entities over time as quantized into intervals also can be used to detect specific entity behavior that also can be published to downstream analytic applications.
While spatial, temporal, and motion processing data are considerably valuable and telling, organizations that collect such data must be very careful in terms of privacy, civil liberty protections, and how that data may be revealed. On the one hand, organizations can apply such data to provide locale-aware customer services (i.e. Location Based Services (LBS)), fraud detection via motion processing, and geographically-targeted marketing offers for consumers, to name a few possibilities. On the other hand, challenges can arise. For example, two different organizations or two different groups within the same organization may each have space and/or time data that they do not want to share freely with each other. Yet it may still be legal and beneficial for both organizations or groups to know whether their respective spatial and/or temporal data concerns a similar event, or near events, without revealing to one another the actual spacetime coordinates (e.g., a longitude, latitude, and a datetime value) of those events. Thus, there is a need to protect the spatial and temporal data that is collected and to privacy-enhance spatial and temporal data when it is shared or compared between parties.